68 research outputs found
Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout
Heart-rate estimation is a fundamental feature of modern wearable devices. In
this paper we propose a machine intelligent approach for heart-rate estimation
from electrocardiogram (ECG) data collected using wearable devices. The novelty
of our approach lies in (1) encoding spatio-temporal properties of ECG signals
directly into spike train and using this to excite recurrently connected
spiking neurons in a Liquid State Machine computation model; (2) a novel
learning algorithm; and (3) an intelligently designed unsupervised readout
based on Fuzzy c-Means clustering of spike responses from a subset of neurons
(Liquid states), selected using particle swarm optimization. Our approach
differs from existing works by learning directly from ECG signals (allowing
personalization), without requiring costly data annotations. Additionally, our
approach can be easily implemented on state-of-the-art spiking-based
neuromorphic systems, offering high accuracy, yet significantly low energy
footprint, leading to an extended battery life of wearable devices. We
validated our approach with CARLsim, a GPU accelerated spiking neural network
simulator modeling Izhikevich spiking neurons with Spike Timing Dependent
Plasticity (STDP) and homeostatic scaling. A range of subjects are considered
from in-house clinical trials and public ECG databases. Results show high
accuracy and low energy footprint in heart-rate estimation across subjects with
and without cardiac irregularities, signifying the strong potential of this
approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at
Elsevier Neural Network
Novel neural approaches to data topology analysis and telemedicine
1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen676. INGEGNERIA ELETTRICAnoopenRandazzo, Vincenz
A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy
Background: Cardiac resynchronization therapy (CRT) has emerged as an
effective treatment for heart failure patients with electrical dyssynchrony.
However, accurately predicting which patients will respond to CRT remains a
challenge. This study explores the application of deep transfer learning
techniques to train a predictive model for CRT response. Methods: In this
study, the short-time Fourier transform (STFT) technique was employed to
transform ECG signals into two-dimensional images. A transfer learning approach
was then applied on the MIT-BIT ECG database to pre-train a convolutional
neural network (CNN) model. The model was fine-tuned to extract relevant
features from the ECG images, and then tested on our dataset of CRT patients to
predict their response. Results: Seventy-one CRT patients were enrolled in this
study. The transfer learning model achieved an accuracy of 72% in
distinguishing responders from non-responders in the local dataset.
Furthermore, the model showed good sensitivity (0.78) and specificity (0.79) in
identifying CRT responders. The performance of our model outperformed clinic
guidelines and traditional machine learning approaches. Conclusion: The
utilization of ECG images as input and leveraging the power of transfer
learning allows for improved accuracy in identifying CRT responders. This
approach offers potential for enhancing patient selection and improving
outcomes of CRT
Learning Biosignals with Deep Learning
The healthcare system, which is ubiquitously recognized as one of the most influential
system in society, is facing new challenges since the start of the decade.The myriad of
physiological data generated by individuals, namely in the healthcare system, is generating
a burden on physicians, losing effectiveness on the collection of patient data. Information
systems and, in particular, novel deep learning (DL) algorithms have been prompting a
way to take this problem.
This thesis has the aim to have an impact in biosignal research and industry by
presenting DL solutions that could empower this field. For this purpose an extensive study
of how to incorporate and implement Convolutional Neural Networks (CNN), Recursive
Neural Networks (RNN) and Fully Connected Networks in biosignal studies is discussed.
Different architecture configurations were explored for signal processing and decision
making and were implemented in three different scenarios: (1) Biosignal learning and
synthesis; (2) Electrocardiogram (ECG) biometric systems, and; (3) Electrocardiogram
(ECG) anomaly detection systems. In (1) a RNN-based architecture was able to replicate
autonomously three types of biosignals with a high degree of confidence. As for (2) three
CNN-based architectures, and a RNN-based architecture (same used in (1)) were used
for both biometric identification, reaching values above 90% for electrode-base datasets
(Fantasia, ECG-ID and MIT-BIH) and 75% for off-person dataset (CYBHi), and biometric
authentication, achieving Equal Error Rates (EER) of near 0% for Fantasia and MIT-BIH
and bellow 4% for CYBHi. As for (3) the abstraction of healthy clean the ECG signal
and detection of its deviation was made and tested in two different scenarios: presence of
noise using autoencoder and fully-connected network (reaching 99% accuracy for binary
classification and 71% for multi-class), and; arrhythmia events by including a RNN to the
previous architecture (57% accuracy and 61% sensitivity).
In sum, these systems are shown to be capable of producing novel results. The incorporation
of several AI systems into one could provide to be the next generation of
preventive medicine, as the machines have access to different physiological and anatomical
states, it could produce more informed solutions for the issues that one may face in the
future increasing the performance of autonomous preventing systems that could be used
in every-day life in remote places where the access to medicine is limited. These systems will also help the study of the signal behaviour and how they are made in real life context
as explainable AI could trigger this perception and link the inner states of a network with
the biological traits.O sistema de saúde, que é ubiquamente reconhecido como um dos sistemas mais influentes
da sociedade, enfrenta novos desafios desde o ínicio da década. A miríade de dados fisiológicos
gerados por indíviduos, nomeadamente no sistema de saúde, está a gerar um fardo
para os médicos, perdendo a eficiência no conjunto dos dados do paciente. Os sistemas de
informação e, mais espcificamente, da inovação de algoritmos de aprendizagem profunda
(DL) têm sido usados na procura de uma solução para este problema.
Esta tese tem o objetivo de ter um impacto na pesquisa e na indústria de biosinais,
apresentando soluções de DL que poderiam melhorar esta área de investigação. Para
esse fim, é discutido um extenso estudo de como incorporar e implementar redes neurais
convolucionais (CNN), redes neurais recursivas (RNN) e redes totalmente conectadas para
o estudo de biosinais.
Diferentes arquiteturas foram exploradas para processamento e tomada de decisão de
sinais e foram implementadas em três cenários diferentes: (1) Aprendizagem e síntese de
biosinais; (2) sistemas biométricos com o uso de eletrocardiograma (ECG), e; (3) Sistema
de detecção de anomalias no ECG. Em (1) uma arquitetura baseada na RNN foi capaz
de replicar autonomamente três tipos de sinais biológicos com um alto grau de confiança.
Quanto a (2) três arquiteturas baseadas em CNN e uma arquitetura baseada em RNN
(a mesma usada em (1)) foram usadas para ambas as identificações, atingindo valores
acima de 90 % para conjuntos de dados à base de eletrodos (Fantasia, ECG-ID e MIT
-BIH) e 75 % para o conjunto de dados fora da pessoa (CYBHi) e autenticação, atingindo
taxas de erro iguais (EER) de quase 0 % para Fantasia e MIT-BIH e abaixo de 4 % para
CYBHi. Quanto a (3) a abstração de sinais limpos e assimptomáticos de ECG e a detecção
do seu desvio foram feitas e testadas em dois cenários diferentes: na presença de ruído
usando um autocodificador e uma rede totalmente conectada (atingindo 99 % de precisão
na classificação binária e 71 % na multi-classe), e; eventos de arritmia incluindo um RNN
na arquitetura anterior (57 % de precisão e 61 % de sensibilidade).
Em suma, esses sistemas são mais uma vez demonstrados como capazes de produzir
resultados inovadores. A incorporação de vários sistemas de inteligência artificial em
um unico sistema pederá desencadear a próxima geração de medicina preventiva. Os
algoritmos ao terem acesso a diferentes estados fisiológicos e anatómicos, podem produzir
soluções mais informadas para os problemas que se possam enfrentar no futuro, aumentando o desempenho de sistemas autónomos de prevenção que poderiam ser usados na vida
quotidiana, nomeadamente em locais remotos onde o acesso à medicinas é limitado. Estes
sistemas também ajudarão o estudo do comportamento do sinal e como eles são feitos no
contexto da vida real, pois a IA explicável pode desencadear essa percepção e vincular os
estados internos de uma rede às características biológicas
Driver-centered pervasive application for heart rate measurement
People spend a significant amount of time daily in the driving seat and some health complexity is possible to happen like heart-related problems, and stroke. Driver’s health conditions may also be attributed to fatigue, drowsiness, or stress levels when driving on the road. Drivers’ health is important to make sure that they are vigilant when they are driving on the road. A driver-centered pervasive application is proposed to monitor a driver’s heart rate while driving. The input will be acquired from the interaction between the driver and embedded sensors at the steering wheel, which is tied to a Bluetooth link with an Android smartphone. The driver can view his historical data easily in tabular or graph form with selected filters using the application since the sensor data are transferred to a real-time database for storage and analysis. The application is coupled with the tool to demonstrate an opportunity as an aftermarket service for vehicles that are not equipped with this technology
Biopotential signals and their applicability to cibersecurity problems
Biometric systems are an uprising technique of identification in today’s
world. Many different biometric systems have been used in everyone’s
daily life in the past years, such as fingerprint, face scan, ECG, and others.
More than 20 years evince that the Elektrokardiogramm (EKG) or Electrocardiogram
(ECG) is a feasible method to perform user identification as each
person has their unique and inherent Elektrokardiogramm (EKG). A biometric
system is based on something that every human being is and cannot lose
or possess as it is an eye, the DNA, palm print, vein patterns, iris, retina,
etc. For this reason, during the last decade, biometric identification or authentication
has gained ground between the classic authentication systems as
it was a PIN or a physical key. All biometric systems, to be accepted, must
fulfill a set of requirements including universality, uniqueness, permanence,
and collectability. The EKG is a biometric trait that not only fulfills those
requirements but also has some advantages over other biometric traits. To
use an EKG as the biometric trait for identification is motivated by four key
points: 1) the collection of an EKG is a non-invasive technique so may contribute
to the acceptability among the population; 2) a human being can only
be identified if they are alive as their heart must be beating; 3) all living
beings have their EKG so it is inclusive; 4) an EKG not only provides identification
but also provides a medical and even emotional diagnose.
There exist many works regarding user identification with EKGs in the
current state-of-the-art. Biometric identification with EKGs has been deployed
using many different techniques. Some works use the fiducial points
of the EKG signal (T-peak, R-peak, P-onset, QRS-offset, ...) to perform the
user identification and others use feature extraction performed by a Neural
Network as the classification or identification method. As the EKG is a signal
which is expressed in time and frequency, many different Neural Network
models can exploit the dissimilarity between each EKG signal from each user
to perform user identification such as Recurrent Neural Networks, Convolutional
Neural Networks, Long-Short Term Memory, Principal Component
Analysis, among others offering very competitive results.
Focusing on user identification, depending on the user condition in each
case, as has been commented before, the EKG not only contributes as an
identification method but also offers a diagnosis as it is a person’s condition
from a medical point of view or a person’s status regarding their emotional
state. Some research has studied certain conditions such as anxiety over EKG
identification showing that higher heart rates might be more complex to identify individuals.
Nevertheless, there are some drawbacks in the current state-of-the-art regarding
identification with EKG. Many systems use very complexly Deep
Learning architectures or, as commented, extract the features by a fiducial
analysis making the biometric system too complex and computationally costly.
One important flaw, not only in biometric systems but in science, is the lack
of publicly available datasets and the use of private ones to perform different
studies. Using a private database for any research makes the experiments and
results irreproducible and it could be considered a drawback in any science
field. Furthermore, many of these works use the EKG signal in a sense that
it can be recovered from the identification system so there is no privacy protection
for the user as anyone could retrieve their EKG signal.
Owing to the many drawbacks of a biometric system based on ECG signals,
ELEKTRA is presented in this thesis as a new identification system whose
aim is to overcome all the inconveniences of the current proposals. ELEKTRA
is a biometric system that performs user identification by using EKGs
converted into a heatmap of a set of aligned R-peaks (heartbeats), forming a
matrix called an Elektrokardiomatrix (EKM).
ELEKTRA is based on past work where the EKM was already created
for medical purposes. As far as the literature covers up to this date, all the
existing research regarding the use of the EKM is focused on the diagnosis
of different Cardiovascular Disease (CVD) such as Congestive Heart Failure,
Atrial Fibrillation, and Heart Rate Variability, among others. Therefore, the
work presented in this thesis, presumably, is the first one to use the EKM as
a valid identification method.
In aim to offer reproducible results, four different public databases are
taken to show the model feasibility and adaptability: i) the Normal Sinus
Rhythm Database (NSRDB), ii) the MIT-BIH Arrhythmia Database (MITBIHDB),
iii) the Physikalisch-Technische Bundesanstalt (PTBDB), and iv)
the Glasgow University Database (GUDB). The first three of them (i, ii and
iii) are taken from Physionet a freely-available repository with medical research
data, managed by the MIT Laboratory. However, the fourth database
(iv ) is also freely available by petition to Glasgow University.
Furthermore, to test ELEKTRA’s adaptability and feasibility of the biometric
system presented, four different datasets are built from the databases
where the EKG signals are segmented into windows to create several Elektrokardiomatrix
(EKM)s. The number of EKMs built for each dataset will
depend on the length of the records. For example, for the Normal Sinus
Rhythm Database (NSRDB) as the EKG records are very extensive, 3000
EKMs or images per user will be obtained. However, for the three other databases, the highest possible number of EKM images is obtained until the
signal is lost. It is important to take into account that depending on the number
of heartbeats taken to be represented in each EKM, a different number
of EKMs is obtained for the three databases in which EKG recordings are
shorter. As higher the number of heartbeats o R-peaks taken (i.e., 7bpf), the
fewer images will be obtained.
Once the datasets of EKMs are constructed, a simple yet effective Convolutional
Neural Network (CNN) is built by one 2D Convolution with ReLU
activation, a max-pooling operation followed by a dropout to include regularisation
and, and finally, a layer with flattened and dense operations with
a softmax or sigmoid function depending if the classification task is categorical
o binary to achieve the final classification. With this simple CNN, the
feasibility and adaptability of ELEKTRA are demonstrated during all the
experiments.
The four databases are tested during chapters 3, 4, and 5 where the experimentation
takes place. In Chapter 3, the NSRDB is studied as the baseline
of identification with control users. Different experiments are conducted with
aim of studying ELEKTRA’s behavior. In the first experiments, how many
heartbeats are needed to identify a user and the costs of convergence of the
model depending on the time computing and the number of heartbeats taken
to be represented in the EKM are studied. In this case, similar results are
achieved in all the experiments as results close to 100% of accuracy are obtained.
In the classification of a non-seen user a user, from a different database
that has not been seen in any other experiment, is processed and tested against
the network. The result obtained is that a non-seen user or an impersonator
would only bypass the system one in ten times which can be considered a
low ratio when many systems are blocked after three to five attempts. The
classification of a user is tested to have a closer situation in which a low-cost
sensor is used. For this experiment, an EKG signal is modified by adding
Gaussian noise and then processed as any other signal. As a demonstration
of our robust system, an accuracy of 99% is obtained indicating that a noisy
signal can be processed too. The last experiment over the NSRDB is where
this database is used to test the feasibility of ELEKTRA by testing how many
images or EKM are enough to identify a user. Even though there is a decrease
in accuracy when the number of images used to train the network is decreased
too, a 97% of accuracy is obtained when training the network with only 300
EKMs per user. This chapter concludes that, as shown in all the experiments,
ELEKTRA is a valid and feasible identification method for control users.
The MIT-BIH Arrhythmia Database (MIT-BIHDB) is a database comprising
patients with Arrhythmia and random users, and the Physikalisch-
Technische Bundesanstalt (PTBDB) comprises patients with different CVD
together with healthy users. Hence, the main goal in Chapter 4 is to study the identification system proposed over users with CVD showing ELEKTRA’s
adaptability. First of all, the MIT-BIHDB is tested achieving outperforming
results and showing how ELEKTRokardiomatrix Application to biometric
identification with Convolutional Neural Networks (ELEKTRA) is capable
to identify a pool of users with and without arrhythmia with just a slight
decrease of the network’s accuracy as a 97% of accuracy is obtained. Secondly,
the whole PTBDB is taken to test the biometric system. The result
obtained in this experiment is lower than in the other ones (a 93% of accuracy)
as the number of images used to train the network has suffered a great
decrease compared to the other experiments and 232 users are being studied.
Lastly, ELEKTRA has tested over 162 users from the PTBDB with specific
CVD which, namely, are Bundle branch block, Cardiomyopathy, Dysrhythmia,
Myocardial infarction, Myocarditis, and Valvular heart disease. Through
this experiment, the aim is to see ELEKTRA’s behaviour when only users
with CVD are included. Better results are obtained compared to the last
experiment. It can be owed that the number of users has decreased and that
a CVD makes more unique each EKG as many researchers use the EKM for
diagnosis purposes. The conclusion extracted from all the experiments from
this chapter is that ELEKTRA is capable to identify users with and without
CVD approaching a real-life scenario.
In Chapter 5 the Glasgow University Database (GUDB) is tested to evaluate
the performance of user identification when the users are performing
different activities. The GUDB comprises 25 users performing five different
activities with different levels of cardiovascular effort: sitting, walking on a
treadmill, doing a maths exam, using a handbike, and running on a treadmill.
The proposed biometric system is tested with each of these activities for 3
and 5 bpf achieving different results in each case. For the experiments performed
where an activity requiring lower cardiovascular effort such as sitting
or walking, the accuracy obtained is close to 100% as it is 99.19% for sitting
and 98.59% for walking. Then for the scenarios where higher heartbeat rates
are supposed the experiment results in lower accuracies as it is jogging with
an 82.63% and biking with a 95.51%. For the maths scenario, its outcome
is different; the heartbeat rate for each user could be different depending on
how nervous each user is. Hence, a 94.0% is obtained with this activity. The
conclusion extracted from these first experiments is that it is more complex
to identify users when they are performing an activity that requires a higher
cardiovascular effort and, consequently have a higher heart rate. For the following
experiment, all scenarios have been merged to study the behaviour of
a system that has been trained with users performing different activities. In
this case, the results obtained seemed to be close to the mean of the results obtained
before as the general accuracy for all the scenarios with 3bpf is 91.32%.
For the subsequent experiments, some of the scenarios have been merged into
two different categories. On the one hand, the more calmed activities (sitting
and walking) have been merged in the so-called Low Cardiovascular Activity (LCA) scenario. The accuracy obtained by training and testing with these
two activities together is 97.74% and an EER of 1.01%. On the other hand,
the High Cardiovascular Activity (HCA) scenario is composed by activities
that require a higher cardiovascular effort (jogging and biking). In this case,
the results obtained have decreased compared to the last ones as the accuracy
is 85.71%. It can be noticed that what has suffered a considerable increase is
the False Rejection Rate (FRR) which is 14.17% without implying an increase
in the False Acceptance Rate (FAR) which is still very low as it is 0.6%. The
last experiments have been called fight of scenarios as there is a confrontation
between scenarios by merging some of them and training with some activities
or scenarios and predicting with different ones. The first experiments that can
be found in this section are training with the LCA group and testing with
the HCA group and vice versa. The results here show a great decrease in the
performance as accuracies are 37.24% and 46.42%, respectively. This fact implies
that it is more complex to identify users that have been registered with
a different heartbeat rate. Last but not least, there are a set of experiments
where the activities have been confronted such as training the network with
the sitting scenario and testing with the jogging scenario. These experiments
confirm the hypothesis for higher heart rates, are more complex to identify
users, and even more when the network has been trained over calmed users.
Even though, one of the main advantages of the presented model is that, even
for low accuracies, the False Acceptance Rate has not increased compared to
the other experiments meaning that an impostor could not achieve bypassing
the system.
Lastly, in Chapter 6 conclusions and discussions are offered. A comparison
between ELEKTRA and other biometric systems based on EKGs from
the current state-of-the-art is offered. These researches from the literature
are examined to show how ELEKTRA outperforms all of them in regards to
some of the aspects such as efficiency, complexity, accuracy, error rates, and
reproducibility among others. It is important to remark that, compared to the
other works, in all experiments performed in this doctoral thesis, really high
performances with high accuracies and low error rates are achieved. In fact,
what is remarkable is that this performance is obtained using a very simple
CNN conformed by just one convolutional layer. By achieving outstanding
results with a simple neural network, the solidity of ELEKTRA is proven.
By this, ELEKTRA contributes to the state-of-the-art by providing a new
method for user identification with EKGs with many benefits. Outstanding
results in terms of high accuracy and low error rates in the experiments assure
the efficiency of ELEKTRA. The fact that the databases used to perform
the experimentation in this doctoral thesis are publicly available, makes this
work reproducible in contrast to many other works in the literature. In fact,
as the databases used are different depending on the users’ nature conforming
to each database, it is established that the identification method proposed is inclusive as all living beings have their own EKG and high accuracies are also
obtained when testing the model over users with different CVD. Moreover, as
it has been proven that users with CVD can also be identified without having
major drawbacks, ELEKTRA offers an identification system that can also
offer a diagnosis of the user who is being identified in terms of their medical
health. In addition, thanks to the GUDB, ELEKTRA can determine for the
first time, as far as the literature reaches, that performing user identification
with EKGs over users performing activities requiring a higher cardiovascular
effort and consequently having higher heartbeat rates, is more complex.
In conclusion, by the studies and experiments performed in this doctoral
thesis, it can be assumed that ELEKTRA is a feasible and efficient identification
method for biometrics with EKG and outperforms the current stateof-
the-art proposals in user identification with EKG.Los sistemas biométricos son una técnica de identificación en auge en la
actualidad. En los últimos años se han utilizado muchos sistemas diferentes
en la vida cotidiana, como la huella dactilar, el escáner facial, o el ECG,
entre otros. De hecho, son más de 20 años los que avalan que el Elektrokardiogramm
(EKG) o el Electrocardiogram (ECG) es un método fiable para
realizar identificación de usuarios. En esta tesis se propone un nuevo método
de identificación biométrica denominado ELEKTRA. Por otro lado, existen
algunos inconvenientes en el estado del arte actual respecto a la identificación
con EKG. Muchos sistemas utilizan arquitecturas muy complejas de Deep
Learning o extraen las características importantes mediante un análisis fiduciario,
haciendo que el sistema biométrico sea demasiado complejo o costoso.
Un fallo importante, no solo en los sistemas biométricos, es la falta de bases
de datos públicas y el uso de bases de datos privadas para la investigación. El
uso de bases de datos privadas en cualquier estudio hace que los experimentos
y los resultados sean irreproducibles y son un inconveniente en cualquier
campo de la ciencia.
En esta tesis doctoral se ha desarrollado ELEKTRA, un sistema de identificación
biométrica, mediante el uso de imagénes llamadas Elektrokardiomatrix
(EKM). Estas imágenes se construyen a partir de realizar un mapa de
calor de un conjunto de picos R (latidos) alineados, formando una matriz.
Con el fin de ofrecer resultados reproducibles, se usan cuatro diferentes bases
de datos públicas para demostrar la viabilidad y adaptabilidad del modelo:
la Normal Sinus Rhythm Database (NSRDB), la MIT-BIH Arrhythmia
Database (MIT-BIHDB), la Physikalisch-Technische Bundesanstalt (PTBDB)
y la Glasgow University Database (GUDB). Se han creado nuevas sub-bases
de datos de EKMs a partir de cada una de las bases de datos mencionadas.
Además, para testear la adaptabilidad y viabilidad de ELEKTRA como sistema
biométrico se construye una CNN sencilla, pero eficaz, con una sola capa
Convolucional.
Las cuatro bases de datos anteriormente mencionadas se han testeado en
los Capítulos 3, 4 y 5. En el Capítulo 3 se estudia la NSRDB como prueba
de concepto de identificación en usuarios control. Se realizan diferentes experimentos
con el objetivo de estudiar el comportamiento de ELEKTRA. Las
características estudiadas con esta base de datos son: cuántos latidos son
necesarios para identificar a un usuario; los costes de convergencia del modelo
presentado; la clasificación de un usuario jamás visto proveniente de una base
de datos diferente; la clasificación de un usuario cuya señal EKG ha sido modificada añadiendo ruido Gaussiano; y la viabilidad de ELEKTRA probando
cuántas imágenes o EKM son suficientes para identificar a un usuario.
En cuanto a las bases de datos que contienen usuarios con CVD, la MITBIHDB
contiene pacientes con Arritmia y usuarios sanos, y la PTBDB contiene
pacientes con diferentes CVD junto a usuarios sanos. Estas dos bases
de datos se estudian en el Capítulo 4, donde se estudia la adaptabilidad de
ELEKTRA a distintas CVDs. En primer lugar, se testea la MIT-BIHDB logrando
resultados prometedores y mostrando cómo ELEKTRA es capaz de
identificar usuarios con y sin arritmia en el mismo grupo. En segundo lugar,
se toma la PTBDB completa obteniendo porcentajes altos de acierto y bajos
en cuanto a tasas de error concierne. Y por último, se prueba ELEKTRA
sobre algunos usuarios con CVD específicos de la PTBDB para ver su comportamiento
cuando sólo se incluyen usuarios con CVD. El resultado de estos
experimentos muestra cómo ELEKTRA es capaz de identificar a los usuarios
con y sin CVD acercándose a un escenario real.
Por último, en el capítulo 5 se prueba ELEKTRA sobre la GUDB para
evaluar el rendimiento de la identificación de usuarios cuando éstos realizan
diferentes actividades cardiovasculares. La GUDB consta de 25 usuarios que
realizan cinco actividades diferentes con distintos niveles de esfuerzo cardiovascular
(sentarse, caminar, hacer un examen de matemáticas, usar una bicicleta
de mano y correr en una cinta). El sistema biométrico propuesto
se prueba con cada una de estas actividades para mostrar que es más complejo
identificar a los usuarios cuando realizan una actividad que requiere un
mayor esfuerzo cardiovascular y, en consecuencia, tienen una mayor frecuencia
cardíaca. Los experimentos realizados consisten en fusionar diferentes actividades
para estudiar las diferencias entre las frecuencias cardíacas y cómo la
identificación del usuario está relacionada la misma. El experimento más representativo
se realiza entrenando el modelo con el escenario en el que el usuario
está sentado y realizando la clasificación ciega de usuarios del escenario en el
cual están corriendo. En este experimento, se obtiene una precisión realmente
baja demostrando que para frecuencias de latidos más altas es más complejo
identificar a un usuario. De hecho, una de las principales ventajas del modelo
presentado es que, incluso con una precisión baja, la Tasa de Falsa Aceptación
no ha aumentado en comparación con los otros experimentos, lo que significa
que un impostor no podría conseguir eludir el sistema. Sin embargo, si la
base de datos se lanza sobre todas las actividades fusionadas, se muestran
resultados precisos que ofrecen un modelo inclusivo para entrenar y probar
sobre usuarios que realizan diferentes actividades.
De este modo, ELEKTRA contribuye al estado del arte proporcionando
un nuevo método de identificac
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port
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